Computerized inspection systems for the analysis of moving web materials have proven critical to modern manufacturing operations. The goal of a production line is to produce material which is perfectly uniform and devoid of variability. However, non-uniformity is a common problem when manufacturing web-based materials. This can be caused by any number of process variables or formulation errors. Consequently, it is becoming increasingly common to deploy imaging-based inspection systems that can automatically classify the quality of a manufactured product based on digital images captured by optical inspection sensors (e.g., cameras). Some inspection systems apply algorithms, which are often referred to as “classifiers,” that attempt to assign a rating to each captured digital image (i.e., “sample”) indicating whether the sample, or portions thereof, is acceptable or unacceptable, in the simplest case.
These inspection systems often attempt to identify “point” defects in which each defect is localized to a single area of the manufactured material. However, other types of defects, referred to “non-uniformity” defects or “patterns” may exist in which the web material exhibits non-uniform variability over a large area. Examples of such non-uniformities include mottle, chatter, banding, and streaks. Non-uniformity-type defects such as these are by definition distributed and non-localized. As a result, such defects may be more difficult for computerized inspection systems to detect and quantify than localized, point defects.
When attempting to detect non-uniformity defects in manufactured material, the inspection system typically collects and processes sample images to extract features indicative of particular non-uniformities. On the basis of these features, the inspection system applies one or more classifiers to produce an assessment of the severity of the non-uniformity. The feature extraction can be computationally intensive and a limiting factor of the inspection process. For example, in this step, high resolution images containing several million pixels are reduced to perhaps no more than fifty representative numbers (or features) through routines that may involve filtering, morphological, temporal, spectral, or statistical processing. The resulting numbers then form the basis for assessing the quality of the underlying product. The amount of time required to collapse millions of pixel values into tens of informative numbers can be substantial and, as such, cannot be performed in real-time for fast production rates, even on modern computers. One possibility could be to purchase higher quantities of more expensive computers, but this solution may make the cost of the inspection systems prohibitively expensive and gives rise to additional implementation problems of data distribution and result aggregation.